Recently, sentiment classifiers pre-trained on a language modeling task have lead to state-of-the-art results [1]}, [2]}, [3]}. This improvement suggests that sentiment analysis benefits from learning word order and fine-grained relationships between tokens, which can be gleaned from unlabeled data. These approaches, however, have only been applied in a monolingual setting and it is not clear how the difference in word orders would affect them in a cross-lingual setup. In this work, we perform an analysis of the effect of word order on cross-lingual sentiment classifiers that use bilingual embeddings as features. We show that these models are sensitive to word order and benefit from pre-reordering the target-language test data so that it resembles the source-language word order.
